创伤脓毒症风险预警诊断和预后评估模型建立与评价

Construction and evaluation of models for early diagnosis and prognosis assessment of trauma sepsis risks

目的联合临床常用的检测指标和生物评分构建脓毒症早期预警诊断和预后评估模型，并探讨模型预警及预后评估的价值。方法采用回顾性对照研究分析2010年1月-2016年5月收治的209例严重创伤患者的临床资料，采集入院当天、伤后3，5，7d患者的白细胞计数、淋巴细胞计数及百分数、单核细胞计数及百分数、中性粒细胞计数及百分数、中性粒细胞计数与淋巴细胞计数的比值（N／L）以及入院当天的急性生理和慢性健康评估Ⅱ（APACHEⅡ）评分、感染相关序贯性脏器功能衰竭评分（SOFA）和改良早期预警评分（MEWS）、格拉斯哥昏迷评分（GCS）、多器官功能障碍综合征（MODS）评分、乳酸（LAC）等数据，构建早期预警的加权模型和预后评估的生物评分模型，并应用受试者工作特征（ROC）计算曲线下面积（AUC），评价其在脓毒症预警诊断和预后评估中的效果。结果人院当天，由APACHEⅡ评分、SOFA和MEWS联合的加权模型AUC为0．729。伤后3d炎性细胞联合加权诊断模型AUC为0．680，生物评分AUC为0．800，差异有统计学意义（P〈0．05）。伤后5d炎性细胞联合加权诊断模型AUC为0．789，生物评分AUC为0．812，差异有统计学意义（P〈0．05）。伤后7d炎性细胞联合加权诊断模型AUC为0．706，生物评分AUC为0．713，差异无统计学意义（P〉0．05）。伤后3，5d的生物评分AUC差异有统计学意义（P〈0．05）。由APACHEⅡ评分、MODS评分、GCS和LAC联合的加权模型应用于脓毒症预后评估时，入院当天的AUC为0．838，伤后3d的AUC为0．878，伤后5d的AUC为0．947，伤后7d的AUC为0．936。人院当天、伤后3，5，7d的AUC差异有统计学意义（P〈0．05）。结论伤后3d的生物评分对脓毒症有较好的早期预警效果。伤后5d由APACHEⅡ评分、MODS评分、GCS和LAC联合的加权模型能有效预测脓毒症患者的预后。

Objective To investigate the value of muhiple inflammatory cells and clinical score in early diagnosis and prognosis assessment of trauma sepsis risks. Methods This retrospective control study enrolled 209 severe trauma patients admitted from January 2010 and May 2016. White blood cell count, lymphocyte count and percentage, monocyte count and percentage, neutrophil count and percentage, ratio of neutrophil to lymphocyte count （N/L）, acute physiology and chronic health evaluation （APACHE） Ⅱ score, sequential organ failure assessment （SOFA）, improved early warning score （MEWS）, Glasgow coma score （GCS）, multiple organ dysfunction syndrome （MODS） score and lactic acid （LAC） were collected on the day of admission and 3, 5, 7 days after trauma. These data were applied to construct weighted and biological score models for early diagnosis and prognosis of traumatic sepsis. Receiver operating characteristic curve （ROC） was performed and area under the curve （AUC） was calculated to measure the value of the two models in early diagnosis and prognosis of sepsis. Results AUC of the weighted model combined by APACHE Ⅱ score, SOFA score and MEWS was 0. 729 on the day of admission. AUC of the weighted model combined by inflammatory ceils was 0. 680 and AUC of the biological score model was 0. 800 3 days after trauma （P 〈 0. 05 ）. AUC of the weighted models combined by inflammatory cells was 0. 798 and AUC of the biological score model was 0. 812 5 days after trauma （P 〈 0. 05）. AUC of the weighted models combined by inflammatory cells was 0. 706 and AUC of the biological score model was 0. 713 7 days after trauma （ P 〉 0.05 ）. AUC of the biological score model had significant difference 3 days and 5 days after trauma （ P 〈 0.05 ）. Of the weighted model combined by APACHE Ⅱ score, MODS score, GCS and LAC to evaluate the prognosis of sepsis, the AUC showed significant difference on the day of admission （0. 838 ）, 3 days after trauma （0. 878 ）, 5 days after trauma （ 0. 947 ） and 7 days after trauma （ 0. 936 ） （ P 〈 0. 05 ）. Conclusions Biological score possesses better effect on early diagnosis of sepsis 3 days after trauma. Weighted model combined by APACHE Ⅱ score, MODS score, GCS and LAC can effectively predict the prognosis of sepsis 5 days after trauma.